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options.py
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# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import os
import argparse
file_dir = os.path.dirname(__file__) # the directory that options.py resides in
class TrainOptions:
def __init__(self):
self.parser = argparse.ArgumentParser(description="options")
# CFG
self.parser.add_argument(
"--config", dest="cfg", help="The config file.", default=None, type=str)
self.parser.add_argument(
"-o", "--opt", nargs='*', help="set configuration options")
# PATHS
self.parser.add_argument("--data_path",
type=str,
help="path to the training data")
self.parser.add_argument("--log_dir",
type=str,
help="log directory")
# TRAINING options
self.parser.add_argument('--num_gpus',
type=int,
help='number of gpus used in training')
self.parser.add_argument("--seed",
type=int,
help='seed used in training.')
self.parser.add_argument("--model_name",
type=str,
help="the name of the folder to save the model in")
self.parser.add_argument("--split",
type=str,
help="which training split to use",
choices=["eigen_lite", "eigen_full"])
self.parser.add_argument("--num_layers",
type=int,
help="number of resnet layers",
choices=[18, 34, 50, 101, 152])
self.parser.add_argument("--freeze_bn",
action='store_true',
help='freeze the running mean and running variance of all bn layers.')
self.parser.add_argument("--dataset",
type=str,
help="dataset to train on",
choices=["kitti", "kitti_odom", "kitti_depth", "kitti_test"])
self.parser.add_argument("--png",
help="if set, trains from raw KITTI png files (instead of jpgs)",
action="store_true")
self.parser.add_argument("--height",
type=int,
help="input image height")
self.parser.add_argument("--width",
type=int,
help="input image width")
self.parser.add_argument("--disparity_smoothness",
type=float,
help="disparity smoothness weight")
# The below three args please config in the yaml file.
# self.parser.add_argument("--scales",
# nargs="+",
# type=int,
# help="scales used in the loss",
# default=[0, 1, 2, 3])
# self.parser.add_argument("--min_depth",
# type=float,
# help="minimum depth",
# default=0.1)
# self.parser.add_argument("--max_depth",
# type=float,
# help="maximum depth",
# default=100.0)
# self.parser.add_argument("--frame_ids",
# nargs="+",
# type=int,
# help="frames to load",
# default=[0, -1, 1])
self.parser.add_argument("--use_stereo",
help="if set, uses stereo pair for training",
action="store_true")
# DEPTH HINT options
self.parser.add_argument("--use_depth_hints",
help="if set, apply depth hints during training",
action="store_true")
self.parser.add_argument("--depth_hint_path",
type=str,
help="path to load precomputed depth hints from. If not set will.be assumed to be data_path/depth_hints")
# OPTIMIZATION options
self.parser.add_argument("--batch_size",
type=int,
help="batch size")
self.parser.add_argument("--learning_rate",
type=float,
help="learning rate")
self.parser.add_argument("--start_epoch",
type=int,
help="number of epochs")
self.parser.add_argument("--num_epochs",
type=int,
help="number of epochs")
self.parser.add_argument("--scheduler_step_size",
type=int,
help="step size of the scheduler")
self.parser.add_argument("--epsilon",
type=float,
help="epsilon in Adam optimizer",
default=0.001)
self.parser.add_argument("--weight_decay",
type=float,
help="weight decay factor for optimization",
default=0.01)
# ABLATION options
self.parser.add_argument("--v1_multiscale",
help="if set, uses monodepthv2 v1 multiscale",
action="store_true")
self.parser.add_argument("--avg_reprojection",
help="if set, uses average reprojection loss",
action="store_true")
self.parser.add_argument("--disable_automasking",
help="if set, doesn't do auto-masking",
action="store_true")
self.parser.add_argument("--predictive_mask",
help="if set, uses a predictive masking scheme as in Zhou et al",
action="store_true")
self.parser.add_argument("--no_ssim",
help="if set, disables ssim in the loss",
action="store_true")
self.parser.add_argument("--weights_init",
type=str,
help="choose from default (paddle pretrained weights), scratch, or a path to a custom weight file.")
self.parser.add_argument("--pose_model_input",
type=str,
help="how many images the pose network gets",
choices=["pairs", "all"])
self.parser.add_argument("--pose_model_type",
type=str,
help="normal or shared",
choices=["posecnn", "separate_resnet", "shared"])
# SYSTEM options
self.parser.add_argument("--num_workers",
type=int,
help="number of dataloader workers")
# LOADING options
self.parser.add_argument("--load_weights_folder",
type=str,
help="name of model to load")
self.parser.add_argument("--models_to_load",
nargs="+",
type=str,
help="models to load",
default=["encoder", "depth", "pose_encoder", "pose"])
# LOGGING options
self.parser.add_argument("--log_frequency",
type=int,
help="number of batches between each console log")
self.parser.add_argument("--visualdl_frequency",
type=int,
help="number of batches between each visualdl log")
self.parser.add_argument("--save_frequency",
type=int,
help="number of epochs between each save")
# EVALUATION options
# Those below args are not support, please config them in the yaml file if you need.
self.parser.add_argument("--eval_stereo",
help="if set evaluates in stereo mode",
action="store_true")
self.parser.add_argument("--eval_mono",
help="if set evaluates in mono mode",
action="store_true")
self.parser.add_argument("--disable_median_scaling",
help="if set disables median scaling in evaluation",
action="store_true")
self.parser.add_argument("--pred_depth_scale_factor",
help="if set multiplies predictions by this number",
type=float)
self.parser.add_argument("--ext_disp_to_eval",
type=str,
help="optional path to a .npy disparities file to evaluate")
self.parser.add_argument("--eval_split",
type=str,
choices=[
"eigen", "eigen_benchmark", "benchmark", "odom_9", "odom_10"],
help="which split to run eval on")
self.parser.add_argument("--save_pred_disps",
help="if set saves predicted disparities",
action="store_true")
self.parser.add_argument("--no_eval",
help="if set disables evaluation",
action="store_true")
self.parser.add_argument("--eval_eigen_to_benchmark",
help="if set assume we are loading eigen results from npy but "
"we want to evaluate using the new benchmark.",
action="store_true")
self.parser.add_argument("--eval_out_dir",
help="if set will output the disparities to this folder",
type=str)
self.parser.add_argument("--post_process",
help="if set will perform the flipping post processing "
"from the original monodepthv2 paper",
action="store_true")
# SUPERVISE options
self.parser.add_argument("--encoder",
type=str,
help='type of encoder',
default='densenet121_bts')
self.parser.add_argument("--max_depth",
type=float,
help='maximum depth in estimation',
default=80.0)
self.parser.add_argument('--variance_focus',
type=float,
help='lambda in paper: [0, 1], higher value more focus on minimizing variance of error',
default=0.85)
def parse(self):
self.options = self.parser.parse_args()
return self.options